Computational probabilistic quantification of pro-arrhythmic risk from scar and left-to-right heterogeneity in the human ventricles

Both scar and left-to-right ventricular (LV/RV) differences in repolarization properties have been implicated as risk factors for lethal arrhythmias. As a possible mechanism for the initiation of re-entry, a recent study has indicated that LV/RV heterogeneities in action potential duration (APD) adaptation can cause a transient increase in APD dispersion following rate acceleration, promoting unidirectional block of conduction at the LV/RV junction. In the presence of an ischemic region and ectopic stimulation, a pathological dispersion in repolarization has been suggested to increase the risk of electrical re-entry. However, the exact location and timing of the ectopic activation play a crucial role in initiation of re-entry, and certain combinations may lead to re-entry even under normal LV/RV dispersion in repolarization. This suggests that the phenomenon needs to be investigated in a quantitative way. In this study we employ a computationally efficient, phenomenological model in order to investigate the proarrhythmic properties of a range of combinations of position and timing of an ectopic activation. This allows us to probabilistically study how increasing interventricular dispersion of repolarization increases arrhythmic risk. Results indicate that a larger LV/RV dispersion in repolarization allows ectopic beats to initiate re-entry during a significantly larger time window and from a greater number of locations compared to the case of smaller LV/RV dispersion

Suggesting Cooking Recipes Through Simulation and Bayesian Optimization

Cooking typically involves a plethora of decisions about ingredients and tools that need to be chosen in order to write a good cooking recipe. Cooking can be modelled in an optimization framework, as it involves a search space of ingredients, kitchen tools, cooking times or temperatures. If we model as an objective function the quality of the recipe, several problems arise. No analytical expression can model all the recipes, so no gradients are available. The objective function is subjective, in other words, it contains noise. Moreover, evaluations are expensive both in time and human resources. Bayesian Optimization (BO) emerges as an ideal methodology to tackle problems with these characteristics. In this paper, we propose a methodology to suggest recipe recommendations based on a Machine Learning (ML) model that fits real and simulated data and BO. We provide empirical evidence with two experiments that support the adequacy of the methodology

On the accuracy of the ALI method for solving the radiative transfer equation

We solve the integral equation describing the propagation of light in an isothermal plane-parallel atmosphere of optical thickness $\tau^*$, adopting a uniform thermalization parameter $\epsilon$. The solution given by the ALI method, widely used in the field of stellar atmospheres modelling, is compared to the exact solution. Graphs are given that illustrate the accuracy of the ALI solution as a function of the parameters $\epsilon$, $\tau^*$ and optical depth variable $\tau$.Comment: 7 pages, 11 figures, A&A, accepted 30 July 2003, minor correction

The influence of coronal EUV irradiance on the emission in the He I 10830 A and D3 multiplets

Two of the most attractive spectral windows for spectropolarimetric investigations of the physical properties of the plasma structures in the solar chromosphere and corona are the ones provided by the spectral lines of the He I 10830 A and 5876 A (or D3) multiplets, whose polarization signals are sensitive to the Hanle and Zeeman effects. However, in order to be able to carry out reliable diagnostics, it is crucial to have a good physical understanding of the sensitivity of the observed spectral line radiation to the various competing driving mechanisms. Here we report a series of off-the-limb non-LTE calculations of the He I D3 and 10830 A emission profiles, focusing our investigation on their sensitivity to the EUV coronal irradiation and the model atmosphere used in the calculations. We show in particular that the intensity ratio of the blue to the red components in the emission profiles of the He I 10830 A multiplet turns out to be a good candidate as a diagnostic tool for the coronal irradiance. Measurements of this observable as a function of the distance to the limb and its confrontation with radiative transfer modeling might give us valuable information on the physical properties of the solar atmosphere and on the amount of EUV radiation at relevant wavelengths penetrating the chromosphere from above.Comment: 19 pages, 11 figures (pre-print format). Accepted for publication in Ap

Population of human ventricular cell models calibrated with in vivo measurements unravels ionic mechanisms of cardiac alternans

Cardiac alternansis an important risk factor in cardiac physiology, and is related to the initiation of many pathophysiological conditions. However, the mechanisms underlying the generation of alternans remain unclear. In this study, we used a population of computational human ventricle models based onthe O’Hara model [1] to explore the effect of 11 key factors experimentally reported to be related to alternans. In vivo experimental datasets coming from patients undergoing cardiac surgery were used in the calibration of our in silico population of models. The calibrated models in the population were divided into two groups (Normal and Alternans) depending on alternans occurrence. Our results showed that there were significant differences in the following 5 ionic currents between the two groups: fast sodium current, sodium calcium exchanger current, sodium potassium pump current, sarcoplasmic reticulum (SR) calcium release flux and SR calcium reuptake flux. Further analysis indicated that fast sodium current and SR calcium uptake were the two most significant currents that contributed to voltage and calcium alternans generation, respectively